U.S. and China Flight Delay Datasets(including delay, weather categories, airport features, and aviation network crowdedness matrices)

The U.S. delay dataset is collected from Kaggle(https://www.kaggle.com/datasets/robikscube/flight-delay-dataset-20182022), covering three years of flight data from January 1, 2017, to December 31, 2019. The dataset originally collected includes data from 360 airports. We remove airports with fewer annual flight numbers and select data from 75 medium and large airports for our experiments. The weather dataset summarizes 12 weather categories, including normal weather, light rain, moderate rain, heavy rain, light snow, heavy snow, moderate fog, severe fog, precipitation, storm, hail, and severe cold. The China delay dataset was collected from Xiecheng(https://pan.baidu.com/s/1dEPyMGh\#list/path=\%2F), covering two years of flight data from May 1, 2015, to June 1, 2017. We select airports with a total number of flights exceeding 10000. From related special event data, 10 weather categories are obtained, including normal weather, thunderstorms, cloud, thunder, fog, strong winds, storms, snow, and severe convective weather. During the experiment, we only consider flight records between 6:00 AM and 11:59 PM, as very few flights were observed outside this time frame.

美国延误数据集（U.S. delay dataset）采集自Kaggle平台（https://www.kaggle.com/datasets/robikscube/flight-delay-dataset-20182022），涵盖2017年1月1日至2019年12月31日共计三年的航班运行数据。该原始数据集包含360个机场的航班信息，我们剔除了年航班吞吐量较低的机场，最终筛选出75个大中型机场的相关数据用于本实验。
天气数据集（weather dataset）涵盖12类天气场景，分别为正常天气、小雨、中雨、大雨、小雪、大雪、中度雾霾、重度雾霾、降水、雷暴、冰雹以及极寒天气。
中国延误数据集（China delay dataset）采集自携程（Xiecheng）平台（https://pan.baidu.com/s/1dEPyMGh#list/path=%2F），涵盖2015年5月1日至2017年6月1日共计两年的航班运行数据。我们筛选出总航班量超过10000架次的机场作为研究样本。结合相关专项事件数据，我们整理出10类天气类别，分别为正常天气、雷暴、多云、雷电、雾霾、强风、风暴、降雪以及强对流天气。实验过程中，我们仅纳入每日6:00至23:59之间的航班记录，因该时段外的航班观测数据极为稀少。

搜集汇总

背景与挑战

背景概述

该数据集包含美国和中国的航班延误数据，涵盖了2017-2019年和2015-2017年的航班记录，以及相关的天气类别、机场特征和航空网络拥挤度矩阵。数据集特别关注了中大型机场的航班数据，并排除了特定时间段外的航班记录。